Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Abstract

Virtual learning environments have become widespread in today's society to avoid time and space constraints and to share high-quality learning resources. In the process of human-computer interaction, student behaviors are recorded instantly. This article aims to design an educational recommendation system according to the individual's interests in educational resources, which is evaluated based on clicking or downloading the source and the score given to that source by the user. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of how to be aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: 1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and 2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes, with an average accuracy of 0.9978 and an error of 0.0051 offers more appropriate recommendations than similar articles.